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Pricing greenhouse gas emissions is a risk management problem. It involves making trade-offs between consumption today and unknown and potentially catastrophic damages in the (distant) future. The optimal carbon price is based on society's willingness to substitute consumption across time and across uncertain states of nature. A large body of work in macroeconomics and finance has attempted to infer societal preferences using the observed behavior of asset prices, and has concluded that the standard preference specifications are inconsistent with observed asset valuations. This literature has developed a richer set of preferences that are more consistent with asset price behavior. In this paper, we explore the implications of these richer preference specifications for the Social Cost of Carbon (SCC), the expected discounted damage of each marginal ton of carbon emissions at an optimal emissions reductions pathway. We develop a simple discrete-time model in which the representative agent has an Epstein-Zin preference specification, and in which uncertainty about the effect of carbon emissions on global temperature and on eventual damages is gradually resolved over time. In our model the SCC is equal to the value of the carbon emissions price at any given point in time that maximizes the utility of the representative agent at that time. We embed a number of features including tail risk, the potential for technological change, and backstop technologies. When coupled with the potential for low-probability, high-impact outcomes, our calibration allows us to decompose the SCC into the expected damages and the risk-premium. In contrast to most modeled carbon price paths, our calibration suggests a high SCC today that is expected to decline over time. It also points to the importance of backstop technologies and, in contrast to standard specifications, to potentially very large deadweight costs of delay. We find, for example, that with damage distributions calibrated to an SCC of $40, a value associated with only a small risk premium, the deadweight loss in utility associated with delaying the implementation of optimal pricing by 15 years is equivalent to a 6% loss of consumption.

Zonder onderwerpscode: economie

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Pricing greenhouse gas emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. This setup calls for an optimal control model to determine the carbon dioxide (CO2) price. It also relies on society's willingness to substitute consumption across time and across uncertain states of nature, the forte of Epstein-Zin preference specifications. We develop the EZ-Climate model, a simple discrete-time optimization model in which uncertainty about the effect of CO2 emissions on global temperature and on eventual damages is gradually resolved over time. We embed a number of features including potential tail risk, exogenous and endogenous technological change, and backstop technologies. The EZ-Climate model suggests a high optimal carbon price today that is expected to decline over time as uncertainty about the damages is resolved. It also points to the importance of backstop technologies and to very large deadweight costs of delay. We decompose the optimal carbon price into two components: expected discounted damages and the risk premium.

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This paper offers a multisecurity model in which prices reflect both covariance risk and misperceptions of firms' prospects, and in which arbitrageurs trade to profit from mispricing. We derive a pricing relationship in which expected returns are linearly related to both risk and mispricing variables. The model thereby implies a multivariate relation between expected return, beta, and variables that proxy for mispricing of idiosyncratic components of value tends to be arbitraged away but systematic mispricing is not. The theory is consistent with several empirical findings regarding the cross-section of equity returns, including: the observed ability of fundamental/price ratios to forecast aggregate and cross-sectional returns, and of market value but not non-market size measures to forecast returns cross-sectionally; and the ability in some studies of fundamental/price ratios and market value to dominate traditional measures of security risk. The model also offers several untested empirical implications for the cross-section of expected returns and for the relation of volume to subsequent volatility.